modelling proteomes ram samudrala department of microbiology how does the genome of an organism...

Modelling proteomesRam Samudrala

Department of Microbiology

How does the genome of an organism specify its behaviour and characteristics?

Proteome – all proteins of a particular system

~60,000 in human

~60,000 in rice

~4500 in bacterialike Salmonella andE. coli

Several thousanddistinct sequencefamilies

Modelling proteomes – understand the structure of individual proteins

A few thousanddistinct structuralfolds

Modelling proteomes – understand their individual functions

Thousands ofpossible functions

Modelling proteomes – understand their expression

Different expressionpatterns based ontime and location

Modelling proteomes – understand their interactions

Interactions andexpression patternsare interdependentwith structure andfunction

Protein folding

…-L-K-E-G-V-S-K-D-…

…-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-…

one amino acid

DNA

protein sequence

unfolded protein

native state

spontaneous self-organisation (~1 second)

not uniquemobileinactive

expandedirregular

Protein folding

…-L-K-E-G-V-S-K-D-…

…-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-…

one amino acid

DNA

protein sequence

unfolded protein

native state

spontaneous self-organisation (~1 second)

unique shapeprecisely orderedstable/functionalglobular/compacthelices and sheets

not uniquemobileinactive

expandedirregular

De novo prediction of protein structure

sample conformational space such thatnative-like conformations are found

astronomically large number of conformations5 states/100 residues = 5100 = 1070

select

hard to design functionsthat are not fooled by

non-native conformations(“decoys”)

Semi-exhaustive segment-based foldingEFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK

generatecontinuous , distributionslocal and global moves

… …

minimisemonte carlo with simulated annealingconformational space annealing, GA

… …

filter all-atom pairwise interactions, bad contactscompactness, secondary structure,density of generated conformations

2.52 Å 5.06 Å

Model 1

CASP6 prediction for T0215

Ling-Hong Hung/Shing-Chung Ngan

3.63 Å 5.42 Å

Model 5

CASP6 prediction for T0236

Ling-Hong Hung/Shing-Chung Ngan

2.25 Å 4.31 Å

Model 1

CASP6 prediction for T0281

Ling-Hong Hung/Shing-Chung Ngan

Comparative modelling of protein structure

KDHPFGFAVPTKNPDGTMNLMNWECAIPKDPPAGIGAPQDN----QNIMLWNAVIP** * * * * * * * **

… …

scanalign

refine

physical functions

build initial model

minimum perturbation

construct non-conservedside chains and main chains

graph theory, semfold

de novo simulation

T0247 RAPDF TMscore RMSD MaxSub

cf-model -30.14 0.8448 4.055 0.6563

parent 1 -27.09 0.8391 4.108 0.6446

parent 2 -26.68 0.8318 4.194 0.625

parent 3 -26.59 0.8252 4.197 0.6051

parent 4 -26.25 0.839 3.981 0.6281

parent 5 -18.51 0.8422 3.979 0.6416

CASP6 prediction for T0247

Model 1

Tianyun Liu

Model 1

Parent 1

Parent 2 Parent 3

T0247 RAPDF TM-score RMSD MaxSub

cf-model -37.44 0.8718 2.166 0.7911

parent 1 -34.87 0.8662 2.233 0.7789

parent 2 -33.99 0.8248 2.166 0.7402

parent 3 -36.83 0.8254 2.139 0.7456

CASP6 prediction for T0271

Tianyun Liu

0.45

0.55

0.65

0.75

0.85

0.95

1.05T0

246

T026

8

T023

3

T023

1

T027

7

T026

6

T027

1

T024

7

T026

7

T027

6

T027

4

T026

9

T028

2

T024

4

T021

1

T023

4

T023

2

T024

3

T026

4

T022

9

T020

0

T021

3

T027

9

Target ID

TM

-sc

ore

sCF-models average of parent models

CASP6 overall summaries

Tianyun Liu

Similar global sequence or structure does not imply similar function

Qualitative function classification

Kai Wang

Prediction of HIV-1 protease-inhibitor binding energies with MD

MD simulation time

Cor

rela

tion

coe

ffic

ien

t

ps0 0.2 0.4 0.6 0.8 1.0

1.0

0.5

with MD

without MD

Ekachai Jenwitheesuk

Prediction of inhibitor resistance/susceptibility

Kai Wang / Ekachai Jenwitheesuk

http://protinfo.compbio.washington.edu/pirspred/

Integrated structural and functional annotation of proteomes

structure based methodsmicroenvironment analysis

zinc binding site?

structure comparison

homology function?

sequence based methods

sequence comparisonmotif searches

phylogenetic profilesdomain fusion analyses

+

*

**

*Bioverse

*

*

Assign function toentire protein space:

key paradigm is use ofhomology to transfer information across

organisms

experimental datasingle molecule + genomic/proteomic

+EXPRESSION

+INTERACTION

}

Bioverse – explore relationships among molecules and systems

Jason McDermott/Michal Guerquin/Zach Frazier

http://bioverse.compbio.washington.edu

Bioverse – explore relationships among molecules and systems

Jason McDermott/Michal Guerquin/Zach Frazier

http://bioverse.compbio.washington.edu

Bioverse – explore relationships among molecules and systems

Jason McDermott/Michal Guerquin/Zach Frazier

http://bioverse.compbio.washington.edu

Bioverse – explore relationships among molecules and systems

Jason McDermott/Michal Guerquin/Zach Frazier

http://bioverse.compbio.washington.edu

Bioverse – prediction of protein interaction networks

Jason McDermott

Interacting protein database

protein α

protein β

experimentallydeterminedinteraction

Target proteome

protein A85%

predictedinteraction

protein B90%

Assign confidence based on similarity and strength of interaction

Bioverse – E. coli predicted protein interaction network

Jason McDermott

Bioverse – M. tuberculosis predicted protein interaction network

Jason McDermott

Bioverse – C. elegans predicted protein interaction network

Jason McDermott

Bioverse – H. sapiens predicted protein interaction network

Jason McDermott

Bioverse – network-based annotation for C. elegans

Jason McDermott

Jason McDermottArticulation point proteins

Bioverse – identifying key proteins on the anthrax predicted network

Jason McDermott

Bioverse – identification of virulence factors

Bioverse - Integrator

Aaron Chang

Take home message

Prediction of protein structure, function, and networks may be used to model whole genomes to

understand organismal function and evolution

Acknowledgements

Aaron ChangChuck MaderDavid NickleEkachai JenwitheesukGong ChengJason McDermottKai Wang

Ling-Hong HungMike InouyeMichal GuerquinStewart MoughonShing-Chung NganTianyun LiuZach Frazier

National Institutes of HealthNational Science Foundation

Searle Scholars Program (Kinship Foundation)UW Advanced Technology Initiative in Infectious Diseases

http://bioverse.compbio.washington.eduhttp://protinfo.compbio.washington.edu